ownership structure, liquidity, and firm value...abstract a firm’s ownership structure influences...

Ownership structure, liquidity, and firm value:

Effects of the investment horizon

October 2010

Jun Uno* and Naoki Kamiyama†

* Waseda University, Graduate School of Finance, Accounting and Law．This research is supported by the Financial Service and Innovation Management Research Project, sponsored by the Ministry of Education, Culture, Sports, Science and Technology, FY2008. Correspondence to Jun Uno, 1-4-1 Nihombashi, Chuo-ku, Tokyo 103-0027, Japan; Tel. 81-3-3272-6798; fax 81-3-3272-6783; e-mail [email protected]. † Deutsche Securities Inc. We benefited from the comments and suggestions of Yakov Amihud, Marti G. Subrahmanyam, and Hung Wan Kat. We thank participants at Nippon Finance Association Annual Meeting 2009, The 22nd Australasian Finance and Banking Conference, Asian Finance Association Conference 2010, and 13th Conference of Swiss Society for Financial Market Research.

mailto:[email protected]

Abstract

A firm’s ownership structure influences both its liquidity and value. This paper investigate

the above relation by introducing a new measure of latent investment horizon, a weighted

average investment horizon computed from the firm’s ownership structure and the average

investment horizon of various investor categories. We find that the latent investment horizon

explains differences in liquidity and firm value among firms listed on the Tokyo Stock

Exchange. Empirical results indicate that the longer the investment horizon, the lower the

firm’s liquidity and value. In addition, concentrated ownerships by insider and cross-holding

shareholders can lead to inferior liquidity and firm value.

JEL Classification: G10, G32

Keywords: Ownership structure, liquidity, monitoring, corporate governance, market

microstructure

2

1. Introduction

This paper investigates how ownership structure is related to a firm’s market liquidity and

value. If the market liquidity of the firm’s shares declines due to concentrated ownership, the

value of the firm is expected to decrease. According to Bhide (1993) and Holmstrom and Tirole

(1993), illiquidity may result from increased asymmetric information. On the other hand,

large shareholder faces liquidity constraint to unwind her holdings so that she ought to

strengthen monitoring the firm’s management and contributes to increase a firm’s value.

There is a trade-off between illiquidity and level of corporate governance.

Maug (1998) and Kahn and Winton (1998) suggest that greater liquidity can be an

opportunity for large shareholders to increase their profit by monitoring the firm’s

management. They mention the case where a large shareholder chooses to buy more shares

when the firm’s performance is expected to improve as a result of monitoring activities. The

greater the liquidity, the more shares can be bought in the market due to lower transaction

costs. Thus, a higher concentration of ownership does not necessarily mean a trade-off

between corporate governance and illiquidity. This differs from past views such as in Bhide

(1993), where a stock’s high liquidity renders large shareholders less aggressive in their

monitoring and more likely to sell shares when they find poor performance of the firm’s

management.

Thus prior theoretical literature address the impact of concentrated ownership on a firm’s

market liquidity and value, but does not consider shareholders’ investment horizons which

differ substantially among shareholders. When a firm has many shareholders with a longer

(shorter) investment horizon, its market liquidity diminishes (increases). In this study we

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investigate the relation between the average investment horizon of the entire ownership and

a firm’s liquidity as well as value that Amihud and Mendelson (1986) predicts.

Atkins and Dyl’s (1997) study on the relation between investor average holding period and

liquidity is similar to ours. They find a strong correlation between a stock’s share turnover

and bid-ask spread. Our paper differs from theirs in the following regards.

First, we use the latent investment horizon instead of turnover. Turnover, the authors’ proxy

for the shareholders’ investment horizon, is observed ex post and therefore deviates from the

ex ante average holding period of the firm’s ownership structure, because turnover is largely

affected by short-term trading activity and informational events such as quarterly reports

and takeover bids. Our paper focuses on how ownership structure affects stock liquidity. We

need a proxy for the ex ante average holding period of shareholders that is not computed from

a realization of mixed trading interests. Second, we use a liquidity measure such as Amihud’s

(2002) ILLIQ in addition to the bid-ask spread. ILLIQ is reflected by not only the bid-ask

spread but also market impact and thus represents a wider scope of liquidity, which is

relevant for both small individuals and large institutional investors as an indicator of

transaction cost.

For the ex ante investment horizon, this study calculates a weighted average investment

horizon of a firm’s shareholders for Japanese companies listed in the First and Second

Sections of the Tokyo Stock Exchange (TSE). We follow Mahanti et al. (2008), who were the

first to use latent liquidity to estimate transaction cost of corporate bonds. In their study, the

authors estimate the investment horizon of corporate bondholders using data from custodian

banks. In our case, however, there are no corresponding data available from custodian banks.

Therefore, we estimate the latent investment horizon of each stock from the ownership ratio

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and the market wide investment horizon of investor category, such as foreigners, banks, and

individuals. The average investment horizon of each investor category is computed from the

aggregate amount of the holding and the total trading volume of TSE-listed companies.

By considering the average investment horizon of the ownership structure, we can address

cases in the real economy where, under the same concentrated ownership, a firm’s market

liquidity and value are higher because the weighted average investment horizon of its

shareholders is shorter than others. We show that a firm’s weighted average investment

horizon is highly correlated with market liquidity and firm value. We expect that the shorter

the investment horizon, the higher the liquidity and value of the firm. Additionally, there are

two distinct categories of large shareholders in Japan: foreigners and cross-holders (mochiai).

These investors are opposites in terms of investment horizon and monitoring management.

We shed some light on specific investor categories and whether the size of their presence

affects firm liquidity and value.

Our results are summarized as follows: (1) The longer the latent investment horizon, the

lower the liquidity. (2) Longer investment horizon leads to lower firm value. (3) Investor

category has a distinct effect on liquidity, with foreigners impacting positively and

cross-holders negatively. The results imply that ownership structure relates to a firm’s

liquidity and value. Our results indicate that the higher the proportion of short horizon

shareholders, the higher the firm’s liquidity and value. On the other hand, a firm with a high

proportion of infrequent investors who do not monitor management and facilitate the

entrenchment of current management results in lower firm liquidity and value. Based upon

these results, strengthening cross-holding is an inferior corporate policy, since it ultimately

impairs the liquidity of the entire market.

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The remainder of this paper is organized as follows. Chapter 2 proposes a new approach to the

relation between ownership and liquidity, with a brief overview of previous work. The data

and sample stocks are described in Chapter 3. Chapter 4 presents our empirical results and

Chapter 5 gives our conclusions.

2. A New Approach and Hypotheses

2.1 Prior research

A firm’s ownership concentration influences its liquidity and value. We argue that the manner

in which ownership structure affects liquidity depends upon a weighted average investment

horizon of the firm’s shareholders. If the average investment horizon of the firm is longer,

then the illiquidity of its shares is more severe.

Amihud and Mendelson (1986) model predicts that illiquid stocks are owned by investors with

longer investment horizon. If these investors do not actively monitor a firm’s management

and they do not trade, the firm’s share liquidity remains low. As suggested by Maug (1998),

and Kahn and Winton (1998), if a firm’s shareholders commit to monitoring management,

they trade frequently to maximize their profits from their private information and thus

contribute to improve the stock’s liquidity.

Bhide (1993) and Holmstrom and Tirole (1993) demonstrate a negative correlation between

ownership concentration and firm liquidity. When the founding shareholder owns all of a

firm’s shares, there is no liquidity. If the founding shareholder sells off a small part of the

shares, liquidity improves but monitoring incentives are decreased gradually. This is due to a

free rider problem in which minority shareholders enjoy the monitoring efforts of a large

shareholder. On the other hand, the informational advantage of large shareholders who

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commit to monitoring increases information asymmetry among investors, causing lower

liquidity. Thus, a trade-off exists between liquidity and monitoring. Neither of the authors

considers the investment horizon of the firm’s shareholders.

Kahn and Winton (1998) and Maug (1998) argue that large shareholders might not release

their ownership when market liquidity is high. While a large shareholder continues

monitoring management to improve the firm’s value, more shares can be bought to maximize

profit. The decision on how many shares to add depends upon market liquidity or transaction

costs. The authors also point out that for monitoring to remain profitable, it is crucial that the

monitoring information be accurate. They do not, however, examine the investment horizon of

large shareholders.

A small number of empirical studies have been carried out on the trade-off between liquidity

and corporate governance. Gaspar and Massa (2007) empirically examine the trade-off

between monitoring and liquidity. They show that informed ownership improves governance

and induces value-enhanced decisions, but reduces liquidity due to increased adverse

selection cost. Rubin (2007) finds that liquidity is positively correlated to total institutional

holdings but negatively correlated to institutional block holdings. The level of institutional

holdings proxies for trading activity, and the concentration of ownership, such as block

holdings, proxies for adverse selection costs. Sarin and et al. (2000) analyze ownership

concentration by insiders and by institutional investors. They report decreased liquidity in

both cases: by insiders from increased asymmetric information and by institutional investors

from inventory costs. Garvey and Swan (2002) empirically verify Holmstrom and Tirole’s

(1993) hypothesis with a sample of 1,500 U.S. companies and report that high liquidity has a

positive impact on shareholder value.

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Atkins and Dyl’s (1997) study, which is similar to ours, on the relation between investor

average holding period and the bid-ask spread for NASDAQ and New York Stock Exchange

stocks finds strong evidence that the turnover ratio, computed by dividing a firm’s number of

outstanding shares by its annual trading volume, is related to the bid-ask spread. The

authors do not consider ownership structure, so they do not examine whether higher turnover

is due to a firm’s ownership structure or not. It is important to distinguish latent liquidity

from trading volume. Therefore, we introduce a new measure of the weighted average

investment horizon to determine the level of liquidity for individual stocks.

Effects from investment horizon of institutional investors have mixed results. Yan and Zhang

(2007) conclude that short-term institutions are better informed based upon the facts that

short-term institution’s trading is positively related to future stock return and earnings

surprises. On the other hand, Gaspar et al. (2005) and Chen et al. (2007) report that higher

holdings by long-term investors are associated with improvement of post-event performance

in case of takeover and merger & acquisitions.

With regards to foreign investors, Tesar and Werner (1995) find that the turnover rate on

equity held by non-residents is higher than the overall turnover rate on the domestic market.

Foreigners respond to changes in economic conditions by making frequent and sizable shifts

in their holdings of foreign securities, even though much of this activity has little impact on

net investment position. According to Nitta’s (2000) analysis of data from 1988 to 1997, a

positive correlation exists between the ratio of foreign ownership and management

performance measures such as return on equity (ROE), but a negative correlation is observed

in the case of the cross-holding ownership ratio and management performance.

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In Japan, foreigns and cross-holding shareholders are important constituents in ownership

structure. Foreigners typically have a shorter investment horizon and are apt to monitor

management, they expect to contribute improvement of liquidity and value, whereas

cross-holding shareholders typically have a longer investment horizon and are less active in

monitoring management. We expect that the larger the percentage of foreign ownership, the

higher the firm value; on the other hand, the larger the percentage of cross-holding owners,

the lower the firm value. The Japanese market provides an ideal data set to test the

previously proposed hypothesis.

2.2 Hypotheses

We test the following hypotheses with respect to the relation of ownership structure, liquidity

and firm value.

H1: The longer the investment horizon, the lower the liquidity. If a firm has many long-term

investors, they will trade infrequently such that the liquidity of the stock will remain low.

H2: The higher the ownership concentration, the lower the liquidity. Ownership concentration

is to strengthen information asymmetry, which increases transaction costs, reduce liquidity1.

The proxy of concentration is top30 ownership. We separate the top30 ownership into insider

and non-insider portion to investigate the effect of which is greater.

H3: We test whether investor category who owns large amount of shares affects size and

direction of impact on liquidity. When high cross-holding ratio is associated with high top30

ratio, we expect an increase in illiquidity, whereas when high foreign ownership ratio is

associated with high top30 ratio, we expect decrease in illiquidity. Because foreigners are 1 A definition of Insider is shares held by insiders within top thirty shareholders. That of Top30 extracts insiders’ share ownership from that of top thirty shareholders.

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short horizon investors and sensitive to management entrenchment, their large presence

reduces negative impact on liquidity caused by concentration itself. Cross-holders are

opposite to foreigners with respect to investment horizon and monitoring management.

H4: For firm value, the shorter the horizon, the higher the value due to positive liquidity

effect.

H5: Presence of large shareholders creates tradeoff between monitoring and liquidity because

there are two countervailing effects, negative effect from asymmetric information and positive

effect from monitoring. Which factor has larger effect on corporate value is an empirical

question. Considering investor categories who own large proportion of a firm and whose

investment horizon is significantly different, we may point out factors strongly related to firm

value.

3. The Data

We use four years of data on First and Second Section companies on the TSE, from 2004 to

2007. The sample includes 1,657 to 1,686 stocks for which liquidity and cross-holding data are

fully available. As shown in FigureⅠ, the cross-holding structure of Japanese companies

changed rapidly during this period. The Program for Financial Revival implemented in 2002

accelerated the unwinding of cross-holding by banks, while the presence of foreign investors

rose.

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3.1 Investment horizon

A firm’s investment horizon is estimated from two sources of the data. First, the investment

horizons of an investor group are computed from the aggregated market data provided by the

TSE. Each investor group’s investment horizon is the total trading volume during the year

divided by the average portfolio market value at the start and end of each year. The data

source for stock ownership by investor group is the TSE’s Share Ownership Survey. The

trading amount of each investor group is compiled by the TSE and published annually in its

Investment Trends by Investor Group.2 There are four investor categories used at this stage

of the estimation; foreigners, individuals, non-financial corporations, and a group of financial

institutions (trust banks, insurance companies, and banks).3 The following is the equation for

an investor group j’s investment horizon in year t:

(1) jt

jt

jt

jt

volumetradingtotal

valuemarketportfoliovaluemarketportfolioHorizonInvestment

yen 2

1) ( 1

(Table 1 around here)

Table 1 shows the average investment horizon for each investor group at the aggregate level.

A non-financial corporation’s investment horizon (Horizon) is 5 to 7 years and an insurance

company’s Horizon is 15 to 21 years, which is the most inactive among six investor categories

in Table 1. The Horizon of foreigners is 0.2 to 0.4 year, which means they trade two to five

2 The original monthly data frequency was converted into fiscal years. The ownership survey covers seven local markets in Japan, whereas trading volume covers three major markets. The volume on four local exchanges account for about 0.01% of total trading volume in the entire market. 3 In the classification of financial institutions, the investor categories in annual reports do not match the three subgroups available in the TSE statistics, such as commercial banks, insurance companies, and trust banks. Therefore, we take an average of the investment horizons for commercial banks, insurance companies, and trust banks as the investment horizon for financial institutions when computing the investment horizons of the TSE-listed companies.

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times a year. The Horizon of individuals is 0.3 to 0.5 year. The finding that foreigners’ Horizon

is shorter than that of domestic investors is consistent with Tesar and Werner (1995). The

authors report turnover rates in domestic equity held by foreign residents for five major

countries—including the United States, the United Kingdom, and Japan—and find that

foreign investors transact at a significantly higher rate than domestic investors.

Next, we compute Horizon for firm k in year t as follow:

(2)

4

1,

j

jt

jtk

kt HorizonwHorizon

where j represents one of four investor categories, foreigners, individuals, non-financial

corporations, and financial institutions; is a firm j’s ownership ratio obtained from the

company’s annual report; and is the market-wide investment horizon by investor

group. The data source for ownership structure was QUICK’s AMSUS. Thus, the investment

horizons for all listed firms are estimated based upon companies’ ownership structure

jtkw ,

ktHorizon

4.

4 The following example illustrates the computation of equation (2). In 2007 Sony Corporation’s

ownership ratios of foreigners, individuals, non-financial corporations, and financial

institutions were 50.73, 23.11, 3.10, and 21.57%, respectively. Using the and , we

compute the weighted average of Sony’s investment horizon, which is about 2.8 years:

jtkw ,

jtHorizon

jtkw ,

year2.82.391970.209870.086200.11008

21.57%15.650)16.659(0.9593

1 3.1%6.7723.11%0.37350.73%0.217

HorizonofaverageweightedA

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We use the NEEDS database as an additional source of data on large individual shareholders

such as the founder (owner) of a firm and, national and local governments on the top 30

shareholders list. We use these data to adjust the above computation. Since neither a founder

(owner) nor a government trade like ordinary individual investors, we assign the longest

investment horizon among six sub-investor categories in the same year to their ownership

category.

3.2 Ownership structure

3.2.1 Ownership concentration by insiders and non-insiders

Our measure of ownership concentration is the sum of the top 30 shareholders’ holdings

divided by the total number of shares outstanding. In addition, we partition block-holdings

into its two major components, insiders’ and non-insiders’ equity holdings 5 . Higher

concentration by insiders may be a signal of weak governance, whereas concentration by

non-insider serves as a proxy for the probability that informed investors participate in the

market. The concentration by non-insiders strengthens the severity of adverse selection costs.

3.2.2 Investor category ownership ratio

Equity holding by an investor category is computed as the number of shares owned by each

investor category divided by the total number of shares outstanding. Foreign and individuals

investor are used in a regression analysis.

5 Rubin (2007) uses insider and institutional block holdings to measure concentration. Hartzell and Starks (2003) use a measure that is the top five non-insider institutional investors’ holdings divided by the total number of institutional holdings.

13

3.2.3 Cross-holding

Our measure of cross-holding shareholders is the Mochiai holding ratio of each stock,

estimated by the NLI Research Institute. The Mochiai holding occurs when two listed

companies mutually hold shares, confirmed through disclosed materials.6 An average Mochiai

ratio is around 9% and the maximum is above 50%.7

3.3 Liquidity

Total trading cost consists of bid-ask spread and market impact cost. Goyenko et.al.(2009)

suggests that reliable proxy for spread and market impact are different. We select Amihud

(2002)’s illiquidity measure8 for the proxy of market impact and quoted spread9 for the proxy

of adverse selection cost.

3.3.1 Market impact measure

Amihud measure is the monthly average of the absolute daily return divided by the daily yen

volume. We eliminate stocks traded less than 10 days per month. Here, ILLIQ shows the

relation between price change and volume; it is a rough estimate of spread plus market

impact cost. We use the average relative illiquidity (RILLIQ) for each fiscal year:

(3)

iyD

t ivyd

iyd

iyiy VOL

R

DILLIQ

1

1

6 Alternative measure is the Antei (=stable) holding, where the ownership of a firm’s shares by banks and life insurance companies is disclosed but the firm’s holdings of counterparty shares cannot be confirmed by disclosed materials. Our choice of cross-holding measure does not affect the empirical results. Refer to Nitta (2002) for details. 7 Nitta (2002) shows that the higher the cross-holding ratio, the worse the various managerial indices, such as the ROE. 8 This measure is one of best measure as a proxy for market impact, according to Goyenko et.al.(2009) 9 Quoted spread is widely used as a proxy for adverse selection costs such as Atkins and Dyl (1997) and Rubin (2007).

14

(4)

yN

tiy

iyiy

ILLIQN

ILLIQRILLIQ

1

1

Here, RILLIQ is adjusted for market-wide liquidity changes and converted into a natural

logarithm10. According to the literature, liquidity has a commonality (Chordia et al. 2000),

and its market-wide fluctuation has differential impacts on the price of individual stocks

(Amihud 2002). Thus, RILLIQ is adjusted for a time series variation of market-wide liquidity

changes.

3.3.2 Bid-ask spread

Quoted spread (SPRD) is defined as the difference between lowest ask price and highest bid

price divided by the mid-price of the quotes. SPRDs are calculated every time when best ask

and/or bid changes, we compute a time-weighted average spreads for stock j on day t, and

then average them over a year. We exclude any quotes before the opening price.

(5) 2)(

)(

,,

,,,

tjtj

tjtjtj

BestBidBestAsk

BestBidBestAskSPRD .

3.4 Firm value

Our proxy for firm value is so-called Tobin’s Q defined as follow:

(6) 2)debt earinginterest_b + capital total(

)tearing_debinterest_b + uemarket_valaggregate_(

tj,tj,

tj,tj,, tjQRATIO .

10 The impact of a change in market-wide liquidity on ILLIQ for individual stocks is not uniform: It

disproportionately affects low-liquidity stocks (see Amihud 2002 and Chordia et al. 2000).

15

The financial data for calculating this are from World Scope. When we compute Tobin’s q, the

cross-holding-adjusted aggregate market value is used. 11 Table 2 shows the summary

statistics of these variables.

3.5 Cross-sectional correlations

Table 3(A) shows the cross-sectional correlations among the variables. Horizon is positively

correlated with the logarithm of market capitalization (0.3) and negatively correlated with the

share turnover ratio (0.11), as shown in Table 3(A). The correlation with the ownership ratios

of Mochiai are positive (0.3), meaning that the longer Horizon, the higher the Mochiai ratio.

On the other hand, there is almost no correlation between Horizon and Foreigner (see Table

3(B)).


4. Empirical Analyses

4.1 Ownership structure and illiquidity

First, we test the relation between investment horizon and illiquidity. Illiquidity and

ownership structure may be simultaneously determined. All variables are most likely

endogenous and the estimates based on panel least squares are biased and inconsistent.

Considering these problems, we use a two-stage estimation method with instrumental

variables to obtain a consistent estimate.12 Our basic regression equation is

(7) tjtjtjtjtj sriabledControlValestionVariabcConcentrabHorizonayIlliquidit ,,,,, )(

11 Kobayashi (1990) points out that the Mochiai portion should be subtracted from the aggregate market value to calculate Tobin's q. 12 Woodridge (2002) Chapter 10 -11.

16

A proxy for market illiquidity is RILLIQ and bid ask spread (SPRD). For concentration

measures, TOP30, and a set of INSIDER and NON-INSIDER. are used interchangeably. We

also test the interaction between NON-INSIDER and investor category holdings). The natural

logarithm of a firm’s market value (Log_Size) is added as control variable.13 Our instrumental

variables are the lagged explanatory variables. Heteroskedasticity is corrected by White

diagonal standard errors and covariance corrections.

In model1 of Table4, the coefficient of Horizon and Top30 are positive and significant at the

1% level. It means that the longer the investment horizon, the lower the liquidity, and the

higher the concentration, the lower the liquidity. These findings are consistent with H1 and

H2.

In model2, TOP30 variable is separated into insider and non-insider portion of concentration,

INSIDER and NON-INSIDER respectively. The result shows that both variables have

positive correlation with illiquidity, the coefficient of insider concentration is much larger

than that of non-insider concentration (2.248 vs. 0.379). Insider’s ownership concentration

have a bigger negative impact on liquidity than non-insider’s. It indicates when insiders own

large portion of the company’s shares outstanding, liquidity provided by existing shareholders

are limited to cause large market impact.

Model3 examines whether large presence of specific investor category has relation with

liquidity under the concentrated ownership. We are interested here in the magnitude and

direction of the investor category’s influence. We insert cross-term variables such that

(NON-INSIDER x holding ratio of investor category such as Foreign, Indiv, and Crosshld ) as

explanatory variables. The result shows that Foreign and Indiv are insignificant, but 13 Amihud (2002) shows a high negative correlation (-0.614) between firm size and RILLIQ. In our case the correlation is -0.57.

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Crosshld has a significant positive coefficient with ILLIQ. It means that only case where the

high non-insider concentration is associated with high cross-holding ratio, illiquidity

increases. The result indicates not only how large the concentration but also what types of

investors own such large proportion. Cross-holders own shares not for pure investment

purpose, does not trade, and provide less commitment to monitoring, so that other market

participants see these factor negatively for liquidity. In case of foreigner and individuals, their

short horizon investment style mitigate negative effect from asymmetric information on

liquidity.

Next we use SPRD% to run regression equation (8), where SPRD% is the percentage of

bid-ask spread in year t for firm j. Since the severity of asymmetric information affects the

size of the spread, we expect that the higher the TOP30, the wider the spread. We include

number of trades per day (Trade), relative tick size (Tic/Price)14 as control variables.

In table 5, the coefficient of TOP30 is positive and significant, however that of HORIZON is

insignificant. As Goyenko et.al. (2009) suggest that the results support notion that bid-ask

spread is directly related to adverse selection cost and ownership concentration is more

important than investment horizon. In model2 both of INSIDER and NON-INSIDER show

positive correlation with SPRD%, the coefficient of INSIDER is larger than that of

NON-INSIDER (0.4365 vs. 0.2079). It means that higher concentrations by insiders as well as

non-insiders increase the bid-ask spread which is contrary to Rubin (2007) which reports

negative correlation between insider holdings and bid-ask spread. Our result indicates that

insider holdings are related to asymmetric information.

14 Unlike the New York Stock Exchange, the TSE uses a tick size that is a step function of share price. In the sample period of 2004 and 2007, the tick size is ¥1 for stocks priced below ¥2000, ¥5 for stocks priced between ¥2,001 and ¥3,000, ¥10 for stocks priced between 3,001 and ¥30,000. For further detail, see TSE(2010)

18

Model3 examines investor categories’ effect on liquidity. The coefficients of Foreign x

NON-INSIDER and Crosshld x NON-INSIDER are positive, but that of Indiv x

NON-INSIDER is negative. Large presence of cross-holders and foreigners increase adverse

selection cost, while individuals mitigate it. It shows that foreigners have superior

information which increases information asymmetry among investors.

4.2. Ownership, liquidity, and their effects on firm value

We extend an analysis to the relation among investment horizon, ownership concentration

and firm value. Tobin’s q (QRATIO) is used as a proxy for the firm’s market valuation. The

basic regression equation is

(8) ariablesdControllVesionVariablcConcetratriablebHorizonVaaQRATIO

where horizon variable and concentration variables are same as equation (8) and the debt

asset ratio (Debtasset) and the profit growth rate (Growth) are included as control variables.

Debtasset is calculated as total debt divided by total assets, and Growth is calculated as the

one-year growth rate of the ordinary profit which is equivalent to income before extraordinary

items and taxes. In order to avoid endogeneity problem, we use the two-stage estimation with

instrumental variables to obtain a consistent estimate. Our instrumental variables are the

lagged explanatory variables. A panel regression analysis is carried out according to the result

of the Hausman test which rejects the random effect model for both time and cross section,

and then the fixed time-effect model is selected.15

15 The Hausman test rejects the random effect model at p = 0.0099 with equation (6) and at p =

0.0012 with equation (7).

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The estimated results are shown in Table 6. HORIZON is negatively correlated with the firm

value. It means that longer investment horizon of shareholders has negative impact on firm

value. This is interpreted as illiquidity effect. TOP30 is positively correlated with the firm

value. The result supports the notion that block-holders influence corporate governance and

improve performance. On the issue of trade-off between monitoring and liquidity (H5), this

result indicates that positive impact of monitoring activity is larger than negative impact of

illiquidity caused by concentration16. This is a unique and important finding of this study in

the relation of corporate governance and market microstructure.

In model2 INSIDER and NON-INSIDER are significant. The coefficient of NON-INSIDER is

slightly larger than that of INSIDER (0.0122 vs. 0.0100, respectively). We expect pressure

from non-insider block holder is larger than that from insiders, but the difference is not as

large as expected.

Model3 examine whether specific investor category influences to firm value. The variable of

(NON-INSIDER x investor category such as Foreign, Indiv, and Crosshld ) shows differential

effects on firm value. As we expect, Foreign has a significantly positive relation with Q-ratio,

but Crosshld has a significantly negative relation. The results support Leleux, Vermaelen,

and Banerjee (1995) that analyze the impact of large block-holders on firm performance in

France and find that the identity of the block-holders is crucial.

These findings indicate that ownership structure and its composition affect firm’s valuation.

If there is high crossholding relationship, market participants judge the firm to be lax in

uidity

t 16 As a related study, Gaspar and Massa (2007) examine the trade-off between ownership, liqand firm value. They find that the effect of ownership on Tobin’s Q is not statistically significanafter controlling the endogeneity of ownership by IV estimate.

20

corporate governance and discount the firm’s value. Ignorance of corporate governance

severely deteriorates firm’s valuation as well as liquidity.


4.3 Robustness check,

As a robustness check, we investigate how changes in HORIZON and TOP30 affect liquidity

measures. We will see the lagged relationship between them. When a company’s weighted

average of investment horizon or concentration of ownership changes, existing and new

shareholders must buy or sell shares in the market. It affects our measures of liquidity and

firm value on the same year due to large movement of position made by institutions and

foreign investors. For our purpose to confirm robustness of the relation among ownership,

liquidity and firm value, we should ask the following question. When a firm’s ownership

concentration declines, what happens to liquidity and firm value on the following year? Thus

the equation (9) is estimated with three different dependent variables.

(9)

tjtjtjtjtj riablesControllVabTOPbHORIZONbaValueyIlliquiditVariables ,,31,21,1, 30),(

Dependent variables ⊿Variables a change of ILLIQ, SPRD, and QRATIO. In equation (9)

where ⊿ILLIQ is the change in ILLIQ for stock j from year t-1 to year t. ⊿SPRD% is the

change in percent bid-ask spread of stock j from year t-1 to year t. ⊿QRATIO is the change

in QRATIO of stock j from year t-1 to year t. In addition to ⊿HORIZON and ⊿TOP30, we

21

include ⊿Market_ILLIQ for ⊿ILLIQ, ⊿Tic÷⊿Price

17 for⊿SPRD%, and ⊿Debtasset and

⊿Growth to as control variables,

The results are presented in Table 7. With respect to ⊿ILLIQ , larger ⊿HORIZON causes

greater deterioration on liquidity. Increasing insider ownership is positively correlated with

illiquidity on the next year, increasing non-insider ownership has the opposite effect on

illiquidity.

With respect to ⊿SPRD%, ⊿HORIZON has negative coefficient. It means that increased

short-term shareholders increases adverse selection risk for other market participants. When

insider and non-insider concentration increases, the bid-ask spread widens as well.

With respect to ⊿QRATIO, ⊿Insider and ⊿Non-Insider at t-1 have positive impact on

firm value. This supports the notion that higher concentration improves governance and firm

value. Maug (1998), for instance, suggests that the characteristics of the large shareholders,

such as institutions, and their organizational structure are potentially important aspects for

the success of monitoring activities.⊿HORIZON have negative but insignificant relation with

⊿QRATIO .

In summary, investment horizon and ownership concentration are important factors that

influence both liquidity and value of firm’s share. When they change, there are associated

changes in liquidity and firm value.


17 This is an adjustment of the effects of the TSE’s step-wise tick table.

22

5. Conclusions

We empirically show that the weighted average investment horizon of a firm’s ownership

structure affects its liquidity. In addition, investment horizon and monitoring management

influence a firm’s value.

Our empirical results are summarized in the following four points.

(i) The latent investment horizon relates to a firm’s market liquidity. The latent investment

horizon is computed from the ownership structure and the average investment horizon of the

investor categories. The longer the latent investment horizon, the lower the liquidity.

(ii) Concentrated ownership has negative impact on liquidity. The investor categories

considered in this study are insider, non-insiders, foreign, cross-holding and individuals

When insiders own large proportion of the shares, negative effect is larger compared to the

case where non-insiders own large proportion of the shares. Effects from concentrated

ownership depend upon who owns them. Under the concentrated ownership, the higher the

cross-holding, the lower liquidity. The greater foreign ownership widens SPRD% but not

ILLIQ. This indicates asymmetric information effects is more important for bid-ask spread

measures.

(iii) The investment horizon influence firm value. A shorter investment horizon has a positive

impact on firm value.

(iv) Ownership structure affects firm value by signaling a firm’s corporate governance status.

The higher the cross-holding, the lower the firm value. Monitoring by foreign investors,

however, contribute improvement of market liquidity as well as the firm’s valuation directly.

23

The results support the notion that the composition and investment horizon of ownership

structure influence market liquidity and firm value. It indicates that a weighted average

investment horizon of the firm’s shareholders is an important characteristics of firm’s

ownership structure with respect to liquidity and value.

24

References

Amihud, Y. 2002. Illiquidity and stock returns: Cross-section and time-series effects. Journal

of Financial Markets 5, 31-56.

Amihud, Y., and Mendelson, H. 1986. Asset pricing and the bid–ask spread. Journal of

Financial Economics 17, 223-249

Amihud, Y., and Mendelson, H. 2008. Liquidity, the value of the firm, and corporate finance.

Journal of Applied Corporate Finance 20-2 32-45.

Amihud, Y., Mendelson, H., and Uno, J. 1999. Number of shareholders and stock price:

Evidence from Japan. Journal of Finance 54-3, 1169-1184.

Atkins, A., and Dyl, E. 1997. Transactions costs and holding periods for common stocks.

Journal of Finance 52-1, 309-325.

Bhide, A. 1993. The hidden costs of stock market liquidity. Journal of Financial Economics 34,

31-51.

Chen, X., Harford J., and Li, K. 2007. Monitoring: Which institutions matter?, Journal of

Financial Economics 86, 279-305.

Chordia, T., Roll, R. and Subrahmanyam, A. 2000. Commonality in liquidity, Journal of

Financial Economics 56-1, 3-28.

Davies, J. R., Hillier, D., and McColgan, P. 2005. Ownership structure, managerial behavior

and corporate value. Journal of Corporate Finance 11, 645-660.

Dahlquist, M., and Robertsson, G. 2001. Direct foreign ownership, institutional investors, and

firm characteristics. Journal of Financial Economics 59 413-440.

Gaspar, J., Massa M., and Matos, P., 2005. Shareholder investment horizons and the market

for corporate contorol, Journal of Financial Economics 76, 135-165.

Gaspar, J., and Massa M, 2007. Local ownership as private information: Evidence on the

monitoring-liquidity trade-off, Journal of Financial Economics 83, 751-792.

Glosten, L., and Harris, L., 1988. Estimating the components of the bid/ask spread, Journal of


Glosten, L., and Milgrom, P., 1985. Bid, ask and transaction prices in a specialist market with

heterogeneously informed traders, Journal of Financial Economics 14-1, 71-100.

Goyenko, R.Y., Holden C.W., and Trzcinka C.A., 2009. Do liquidity measures measure liquidity,

Journal of Financial Economics 92, 153-181.

Hartzell J., and Starks L., 2003. Institutional Investors and Executive Compensation,

Journal of Finance 58-6, 2351-2374.

25

Hasbrouck J. 2009. Trading Costs and Return for U.S. Equities: Estimating Effective Costs

from Daily Data. Journal of Finance 64-3, 1445-1477.

Hausman, L. 1982. Specification tests in econometrics. Econometrica 46-6, 909-938.

Holmstrom, B., and Tirole, J. 1993. Market liquidity and performance monitoring. Journal of

Political Economy 101-4, 678-709.

Hotchkiss, E., and Strickland, D. 2003. Does shareholder composition matter? Evidence from

the market reaction to corporate earnings announcements. Journal of Finance 58-4,

1469-1498.

Huang, R., Stoll, H., 1997. The components of the bid-ask spread: A general approach, The

Review of Financial Studies 10-4, 995-1034, 1997

Kahn, C., and Winton, A. 1998. Ownership structure, speculation, and shareholder

intervention. Journal of Finance 53-1, 99-129.

Kang, J., and Stulz, R. M. 1997. Why is there a home bias? An analysis of foreign portfolio

equity ownership in Japan. Journal of Financial Economics 46, 3-28

King, M., and Segal, D. 2008. The long-term effects of cross-listing, investor recognition, and

ownership structure on valuation. Review of Financial Studies 22-6, 2393-2404

Leleux, B., Vermaelen, T., and Banerjee, S., 1995, Large shareholdings and corporate control:

An analysis of stake purchases by French holding companies, Working paper, INSEAD

Mahanti, S., Nashikkar, A., Subrahmanyam, M., Chacko, G., and Mallik, G. 2008. Latent

liquidity: A new measure of liquidity, with an application to corporate bonds. Journal of


Maug, E. 1998. Large shareholders as monitors: Is there a trade-off between liquidity and

control? Journal of Finance 53-1, 65-98.

Morck, R., Nakamura, M., and Shivdasani, A. 2000. Banks, ownership structure, and firm

value in Japan. Journal of Business, 73-4, 539-567

Nitta, K. 2000. Cross-sharing and enterprise management, positive analyses concerning

influence of ownership structure (in Japanese). Security Analysts Journal 38-2, 72-93

Rubin, A. 2007. Ownership level, ownership concentration and liquidity. Journal of Financial

Markets 10, 219-248.

Sarin, A., Shastri, K., and Shastri, K., 2000. Ownership structure and stock market liquidity.

Working paper, University of Pittsburgh, Pittsburgh, Pennsylvania.

26

Tesar, L., and Werner, I. M., 1995. Home bias and high turnover. Journal of International

Money and Finance, 14-4 467-492.

Tokyo Stock Exchange, 2010. Guide to TSE Trading Methodology

Woodridge, J., 2002. Econometric Analysis of Cross Section and Panel Data. MIT Press,

Cambridge, Massachusetts.

Yan, X., and Zhang Z. 2009. Institutional Investors and Equity Returns: Are Short-term

Institutions Better Informed? Review of Financial Studies 22-2, 893-924

27

Figure 1. Ownership ratio trends by investor category. This figure shows the trend in the ownership structures of Japanese companies for the period

2002 to 2007. The foreigner ownership ratio is that at the end of the fiscal year as reported by

QUICK-AMSUS. The antei (stabilizing holding) and mochiai ratios of each stock are

estimated by the NLI Research Institute. A mochiai holding occurs when two listed companies

mutually hold shares, confirmed through disclosed materials. An antei holding refers to cases

where the ownership of a firm’s shares by banks and life insurance companies is disclosed but

the firm’s holdings of the counterparty shares cannot be confirmed by disclosed materials. The

mochiai figures are included in the antei holding ratio. The sample includes stocks in the

First and Second Sections of the TSE.

0%

5%

10%

15%

20%

25%

30%

F2002 F2003 F2004 F2005 F2006 F2007

Foreigner

AnteiMochiai

28

Table 1. Average investment horizon by investor category.

The investment horizon by investor category is computed from the annual turnover ratio, that

is, the average of the aggregated market value of an investor category’s ownership at the start

and end of each year divided by the total trading amount of the investor category during the

year.

FiscalYear

Foreigner IndividualsNon-financial

CorporatesTrust Banks Insurance Banks

2004 0.414 0.531 7.488 1.238 15.451 11.5152005 0.331 0.304 5.711 1.065 18.845 13.1432006 0.303 0.393 6.615 1.206 21.274 17.5682007 0.219 0.373 6.773 0.959 16.659 15.650

Unit: year.

Source: TSE’s Share Ownership Survey,. and the Investment Trends by Investor Category.

29

Table 2. Summary statistics.

This table shows the summary statistics of the explanatory variables. Here, HORIZON is a

weighted average of the investment horizons of sample firms, TOP30 is the sum of the top 30

shareholders’ holdings divided by the total number of shares outstanding, QRATIO is Tobin’s q

= (aggregate market value + interest-bearing debt)/(total capital + interest-bearing debt), where

the aggregate market value is adjusted for the cross-holding proportion of ownership. Crosshld,

Foreign, Indiv are the percentages of ownership of firm j’s investor categories (cross-holding,

foreigner and individuals) at the end of fiscal year t

FY Mean Median Maximum Minimum Std Dev. #Obs.HORIZON ALL 4.81 4.89 10.12 0.28 1.34 6,695

(Year) 2004 4.72 4.82 8.00 0.28 1.24 1,6722005 5.29 5.40 10.12 0.43 1.45 1,6802006 5.29 5.40 10.10 0.43 1.45 1,6802007 4.66 4.83 8.63 0.31 1.30 1,686

TOP30 ALL 50.84 50.60 91.38 0.73 15.74 6,604(%) 2004 51.16 51.11 91.20 4.96 15.64 1,633

2005 51.40 51.08 91.38 3.89 15.71 1,6492006 50.46 50.10 90.19 2.59 15.66 1,6582007 50.36 50.05 88.51 0.73 15.92 1,664

QRATIO ALL 1.25 1.05 18.91 -9.33 0.88 6,6852004 1.22 1.05 12.89 0.22 0.77 1,6572005 1.48 1.22 18.91 -9.33 1.14 1,6672006 1.29 1.10 11.90 -7.50 0.87 1,6762007 1.01 0.89 9.26 0.24 0.59 1,685

Crosshld ALL 9.22 7.34 55.22 0.00 8.55 6,695(%) 2004 9.14 7.40 49.64 0.00 8.32 1,657

2005 9.12 7.35 53.02 0.00 8.42 1,6722006 9.25 7.32 55.22 0.00 8.63 1,6802007 9.36 7.30 53.11 0.00 8.83 1,686

Foreign ALL 12.80 9.48 78.27 0.00 11.91 6,695(%) 2004 12.63 9.17 73.65 0.05 11.81 1,657

2005 12.55 9.22 77.63 0.05 11.68 1,6722006 13.18 9.97 77.45 0.00 12.07 1,6802007 12.84 9.42 78.27 0.00 12.07 1,686

Indiv ALL 32.10 29.46 95.98 1.81 17.06 6,695(%) 2004 32.90 30.57 93.51 2.90 16.76 1,657

2005 31.48 28.58 95.40 2.02 16.74 1,6722006 31.71 29.17 95.98 1.81 17.08 1,6802007 32.32 29.57 95.37 2.85 17.60 1,686

Note: Some of the minimum values of Tobin’s q (QRatio) are negative due to the negative equity capital of the

distressed firm.

30

Table 3 Correlations.

Panel A. Correlation between horizon, size, and turnover.

This table shows the correlation between HORIZON, TOP30, Log market value, and Turnover.

Here, HORIZON is a weighted average of the investment horizons of each firm (equation(2)),

and TOP30 is the sum of the top 30 shareholders’ holdings divided by the total number of

shares outstanding and Market value is the number of shares outstanding multiplied by the

stock price at the end of the fiscal year. Turnover is calculated as the daily trading volume

divided by the number of shares outstanding. The correlation coefficients are estimated

annually and averaged over the years 2004 to 2007.

HORIZON TOP30 Log Size TurnoverHORIZON 1 0.4631 0.2917 -0.1108

TOP30 0.4631 1 0.0130 -0.1347Log Size 0.2917 0.0130 1 0.0350Turnover -0.1108 -0.1347 0.0350 1.0000

Panel B. Correlation between HORIZON and investor category’s holding ratios.

This table shows the correlation between the HORIZON and the holding ratios of investor

categories such as foreign, cross-holding, and individual investors. The correlation coefficients

are estimated annually and averaged over the years 2004 to 2007.

HORIZON Foreign Crosshld IndivHORIZON 1 -0.0298 0.3493 -0.7557

Foreign -0.0298 1 -0.1221 -0.4885Crosshld 0.3493 -0.1221 1 -0.1224

Indiv -0.7557 -0.4885 -0.1224 1

31

Table 4 Panel least squares analysis of illiquidity.

This table shows the relation of illiquidity with investment horizon and the ownership

concentration ratio for stocks in the First and Second Sections of the TSE over the period 2004

to 2007. The results are from the panel least square regressions. The fixed period effect model

is selected as a result of the Hausman test. In order to avoid endogeneity problem, we use the

two-stage estimation with instrumental variables to obtain a consistent estimate. Our

instrumental variables are the lagged explanatory variables. RILLIQj,t is the relative ILLIQ

for firm j defined by equation (3), log_Sizej,t, is the natural logarithm of firm j’s market value

at the end of March, HORIZONj,t is the weighted average of the holding period for firm j’s

stockholders in year t, TOP30 is the sum of the top 30 shareholders’ holdings divided by the

total number of shares outstanding and Crosshldj,t and Foreignj,t are the percentages of

ownership of firm j’s investor categories at the end of fiscal year t. are

parameters to be estimated and

eanddcba ,,,,

is an error term. Heteroskedasticity is corrected by White

diagonal standard errors and covariance corrections.

Dependent Variable: RILLIQ

Model1 Model2 Model3

Coefficient t-value Coefficient t-value Coefficient t-value

HORIZON 0.1724 15.94 0.2751 17.60 0.2656 13.60

TOP30 0.7418 8.22

INSIDER 2.2483 12.97 0.0212 11.92

NON-INSIDER 0.3785 4.03

NON=INSIDER*Foreign -0.0046 -1.52

NON-INSIDER*Indiv 0.0051 1.42

NON=INSIDER*CrossHld 0.0159 5.38

Log Size -1.0736 -146.30 -1.0697 -143.50 -1.0416 -87.60

Intercept 2.0914 24.21 1.6658 16.87 1.6756 10.40

Adjusted R-squared 0.817 0.823 0.823

Observations 4,899 4,899 4,899

32

Table 5 Panel least squares analysis of Bid-Ask Spread

This table shows the relation of bid-ask spread with investment horizon and the ownership

concentration ratio for stocks in the First and Second Sections of the TSE over the period 2004

to 2007. The results are from the panel least square regressions. The fixed period effect model

is selected as a result of the Hausman test. In order to avoid endogeneity problem, we use the

two-stage estimation with instrumental variables to obtain a consistent estimate. Our

instrumental variables are the lagged explanatory variables. SPRD%j,t is time-weighted

quoted bid-ask spread divided by midprice, log_Sizej,t, is the natural logarithm of firm j’s

market value at the end of March, HORIZONj,t is the weighted average of the holding period

for firm j’s stockholders in year t, TOP30 is the sum of the top 30 shareholders’ holdings

divided by the total number of shares outstanding, and Crosshldj,t, Foreignj,t and Indiv j,t are

the percentages of ownership of firm j’s investor categories at the end of fiscal year t.

are parameters to be estimated and eanddcba ,,,, is an error term. Heteroskedasticity is

corrected by White diagonal standard errors and covariance corrections.

Dependent Variable: SPRD%



HORIZON -0.0073 -1.17 0.0058 0.58 0.0045 0.39

TOP30 0.2463 6.00

INSIDER 0.4365 3.99 0.5741 4.65


NON=INSIDER*Foreign 0.0129 8.44

NON-INSIDER*Indiv -0.0087 -4.99

NON=INSIDER*CrossHld 0.0048 2.59

Log Trade -0.3588 -38.25 -0.3573 -39.79 -0.4031 -33.96

TIC/VWAP 1.0237 27.99 1.0376 26.02 1.0870 23.52

Intercept 4.0277 42.77 3.9523 45.89 4.5395 35.86



33

Table 6 Panel least squares analyses of Tobin’s q.

This table shows the relation between firm value and investment horizon, ownership

concentration, and ownership ratios for foreigners, individuals, and cross-holding for First-

and Second-Section stocks of the TSE over the period 2004 to 2007. To avoid any endogeneity

problems, we employ lagged variables as instrumental variables. The fixed period effect

model is selected as a result of the Hausman test. In order to avoid endogeneity problem, we

use the two-stage estimation with instrumental variables to obtain a consistent estimate. Our

instrumental variables are the lagged explanatory variables. Here, QRATIO is Tobin’s q =

(aggregate market value + interest-bearing debt)/(total capital + interest-bearing debt), where

the aggregate market value is adjusted for the cross-holding proportion of ownership. Foreign,

Indiv and Crosshld are the same as in equation (7). Debtasset is calculated as total debt

divided by total assets, and Growth is calculated as the one-year growth rate of the ordinary

profit which is equivalent to income before extraordinary items and taxes. Heteroskedasticity

is corrected by White diagonal standard errors and covariance corrections.

Dependent Variable: QRATIO

Model1 Model2 Model3 Model4

Coefficient t-value Coefficient t-value Coefficient t-value Coefficient t-value

HORIZON -0.2004 -7.09 -0.2130 -5.09 -0.0544 -1.53 -0.0839 -2.20

TOP30 0.0118 6.51

INSIDER 0.0100 2.69 0.0080 2.20 0.0068 1.73


NON=INSIDER*Foreign 0.0002 3.03 0.0001 2.56

NON-INSIDER*Indiv 0.0001 1.43

NON=INSIDER*CrossHld -0.0003 -6.39 -0.0003 -6.27

Debt/Asset -0.0044 -2.74 -0.0044 -2.73 -0.0027 -1.81 -0.0024 -1.72

Growth -1.2369 -2.28 -1.2390 -2.28 -1.1628 -2.26 -1.1564 -2.26

Log_Size 0.1990 12.83 0.1986 12.96 0.1397 5.19 0.1186 5.97

Intercept 1.0916 5.56 1.1441 4.58 0.9661 3.17 1.4066 5.32

F stats 134.6 118.2 100.5 111.5

Prob(F-statistic) 0.000 0.000 0.000 0.000

Observations 4,892 4,892 4,892 4,892

34

Table 7 The effects of changes in liquidity measures

This table shows the effects of changes in HORIZON and Top30 at a prior year to liquidity as

well as firm value. The results are from the panel least squares. In model1 where ⊿ILLIQ

is the change in ILLIQ for stock j from year t-1 to year t. In model2 ⊿SPRD% is the change

in percent bid-ask spread of stock j from year t-1 to year t. Changes in HORIZON, INSIDER

and NON-INSIDER at the previous year are included as explanatory variables. In model1, we

include the change in the market-average ILLIQ (Market_ILLIQ). In model2, we have ⊿Tic

and ⊿Price to adjust the effects from the TSE’s step-wise tick schedule. Definitions of

Debt/Asset and Growth are same as table 6. Heteroskedasticity is corrected by White diagonal

standard errors and covariance corrections.


Dependent Variable: ⊿ILLIQ ⊿SPRD% ⊿QRATIO


⊿HORIZON(-1) -0.0570 -4.41 -0.0371 -3.59 -0.0038 -1.70

⊿INSIDER(-1) 0.0099 2.88 0.0067 3.13 0.0409 3.99

⊿NON-=INSIDER(-1) -0.0083 -2.32 0.0038 2.70 0.0017 4.97

⊿（Tic/Price) 0.9944 111.35 0.0061 0.93

⊿Trade -0.1251 -5.09

⊿Debt/Asset -0.0001 -6.58

⊿Growth -0.2346 -21.59

⊿Market_ILLIQ -32.6696 -8.18

Intercept 30.7375 8.22 0.2365 9.96 -0.2346 -21.59



35

ownership structure, liquidity, and firm value...abstract a firm’s ownership structure influences...

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